Simulating tomorrow's supply chain today

Heavy industries operate equipment having a long life and rely on service parts to maintain operations. Often, stock levels for such parts are chosen to achieve fill rate goals, while supply chain performance is evaluated by speed of service. We resolve this disconnect by linking an existing discrete-event warehouse operations simulation with a new Monte Carlo demand categorization and metrics simulation. In the process, we demonstrate the potential of incorporating data on the current state of the supply chain to eliminate the simulation warm-up period and to predict future system performance against metrics targets. We show that the current stocking policy of the organization in our case study cannot achieve planned metrics and that periodic internal policies, such as budgetary approval, further degrade performance. However, a new inventory segmentation approach with continuous review can achieve targets in one year, lower inventory investment 20%, and enable automated buys for certain parts.